About Chase Hooley

Top 10 Big Data Trends in 2016 for Financial Services

2015 was a groundbreaking year for banking and financial markets firms, as they continue to learn how big data can help transform their processes and organizations. Now, with an eye towards what lies ahead for 2016, we see that financial services organizations are still at various stages of their activity with big data in terms of how they’re changing their environments to leverage the benefits it can offer. Banks are continuing to make progress on drafting big data strategies, onboarding providers and executing against initial and subsequent use cases.

For banks, big data initiatives predominately still revolve around improving customer intelligence, reducing risk, and meeting regulatory objectives. These are all activities large Tier 1 financial firms continue to tackle and will do so for the foreseeable future. Down-market, we see mid-tier and small-tier firms (brokerage, asset management, regional banks, advisors, etc.) able to more rapidly adopt new data platforms (cloud and on-premise) that are helping them leapfrog the architectural complexities that their larger brethren must work against. This segment of the market therefore can move more rapidly on growth, profitability and strategic (conceptual/experimental) projects that are aimed at more immediate revenue contribution, versus the more long-term, compliance and cost-dominated priorities that larger banks are focused on.

The market for data software and services providers is moving closer to a breaking point where banks will need to adopt, on larger scales and with greater confidence, solutions to manage internal operations and client-facing activities. This is not unlike the path we have seen cloud technologies take.

Here are some predictions about how big data technologies are evolving, and how these changes will affect the financial services industry:

Machine learning will accelerate, and will be increasingly applied within the fraud and risk sectors. Data-scientist demand and supply continues to work towards equilibrium. Advanced techniques will start being applied within fraud and risk that improve models and allow acceleration towards more real-time analysis and alerting. This acceleration will come from education and real-world applications of market leaders.

Gaps will become more evident between the leaders and the laggards. Each year we see banks that press the gas pedal and are ready to adopt new technology, and those that remain conservative in their efforts to run/change their organization. The stories and use cases will proliferate and become more varied in 2016, and will lead to increased evidence of strong, observable and benchmarked business returns (not just cost takeout) in the broader market.

Data governance, lineage, and other compliance aspects will become more deeply integrated with big data platforms. In order to find a more complete and comprehensive data solution to manage compliance mandates, many banks develop or purchase point solutions, or they try to use existing legacy platforms that are not able to deal with the data surge. Fortunately, there are an increasing number of improved data governance, lineage and quality solutions for Hadoop. More importantly, these new platforms can reach beyond Hadoop and into traditional/legacy data stores to complete the picture for regulation, and they are doing so with the volume, speed, and detail needed to achieve compliance. In addition, 2016 will continue to see the push for “data lakes” that can serve as converged regulatory and risk (RDARR) hubs.

Financial services organizations are struggling to understand how to leverage IoT data. This is the next wave of hype that is grabbing attention in big data, and questions abound in terms of financial services applications. For some industries (telco, retail, and manufacturing) it is already a reality, and these segments have driven the need for IoT data and forced the current conversation. For banks, will IoT data be used more for ATM or mobile banking? Areas that are worth exploring over the coming year involve multiple streams of activity in real time. For example, real-time, multi-channel activities can use IoT data to offer the right offer and advice to retail banking customers at the right time. Or perhaps we should think about this in reverse, where financial firms could embed their services into the actual “thing” or device or other client touch points, not unlike trading collocation facilities that then report home.

Integration into trade, portfolio management, and advisor applications becomes a more prominent feature for software providers. The drum roll of headlines that relates to “gaining benefit from big data” beats louder. Ultimately, this will be judged by end users in the financial sector and the observed (or unobserved, yet measured) benefits and ease of use. Applications that are built off the core of big data platforms will provide that bridge, and sharpen the spear that is big data. We’ve already seen this push with the likes of market data providers, but not with other business user applications, be it CRM, OMS/EMS, etc.

Risk and regulatory data management continue to be the top big data platform priorities. Growth and customer-centric activities sit atop the list of corporate strategies, and there will be firms that can link those strategies to big data. Regardless of whether your bank is an advanced data-driven firm or not, the evolving nature of regulations and the monster challenge to aggregate risk and move towards predictive analytics is still a ways off, yet it’s still a requirement and acknowledged benefit at the C-suite level. Unless heaven opens and regulators ease up on their requirements, risk and regulatory data managements will still be the major challenges for financial institutions in 2016.

The adoption of Hadoop for R:BASE storage and access will proliferate within financial services. Everyone arrives at the party at different times, and adopting technology is no different. The “long tail” adoption is far from here, but middle market or even small-tier banks will begin to see the benefits Hadoop based on:

Providers that are bundling/integrating and delivering more complete solutions, services and platforms

A community of users that continues to grow and provide the reference base to jump into the pool

Data offload is now a “classic” use of Hadoop (relatively speaking), while the cool kids move on to larger big data playgrounds, and the masses will climb on board for this application of big data.

Financial services “big data killer apps” gain wider recognition in the market. These have been the FinTech incubators over the past two to three years, and they are helping to form the front-end links needed between the end user and the data platforms. Expect to see more banks running proof-of-concepts with these applications, which will validate the software and provide the basis for “complete solutions.” Both the front-end and back-end should be optimized in concert, rather than as separate projects. We see this market rapidly expanding from the service integrator end as well. This will usher in the discussion of how “big data software” vs. “legacy software” will be adopted by banks.

Operations becomes, and always has been, the last frontier that gains traction. As more reliable “big data” platforms emerge, the idea of security masters, deeper metadata enrichment, ontologies, integrating LEI, and other standards becomes a stark reality. The traditional data approaches are valid, yet some of the thinking will need to turn on its head to gain the full leverage of new solutions–for example dealing schemas and data modeling. Further, with the work of big data largely taking form in the front office, marketing and risk, there are obvious and enormous overlaps of data in middle and back office operations that can more easily be used to leverage existing data lake efforts. We expect to see risk assessment and performance-related big data activities in the middle office to rapidly increase. Further, we will see deeper dialogue on how to actually bring back-office functions (reconciliation, corporate actions, etc.) on board as well.

The institutional side of the bank will start to adopt and take cues from the retail line of business on ways to improve understanding, marketing, and targeting of clients. There are certainly some pure B2B firms that are leveraging big data for improved client intelligence, but largely they take a back seat to the retail B2C line of business, be it credit card, retail banking, wealth management or lending. An easy crossover is for fund complexes (large mutual fund managers) to improve data collection from wealth advisor networks and broker interactions, as well as improved product utilization. This is especially important as mutual funds are typically once removed from their retail client base, so understanding their institutional clients (advisors) is vital.

Confidence still remains key for many larger banks and other financial services firms in adopting new provider “big data” solutions. That said, as you look towards 2016, there will be a greater push from management to move “big data” projects out of IT and within the hands of the business user. In order to do so, there will be a host of architectural, functional, speed, availability, and security questions to consider. As always, applying the traditional rigor to new architectural layouts does not change, as cost and sprawl seen in traditional architectures will begin to surface in new Hadoop and converged big data build-outs.

Further, there will and should be strong leverage and utilization of existing staff/processes for activities such as data governance, quality, reference data management, and standards. This will require continued education of all parties, namely those outside of IT, to understand the rapid developments in the marketplace.

Lastly, there will be the growing conversation on what balance of open source and provider solutions makes sense. Not all open source projects are designed to purely fit the needs of the institutional user, yet open source delivers the agility required going forward—each bank’s requirement will vary, and finding the right mix will be vital to accelerate efforts with big data, which is really all data. All told, the market in 2016 will move forward and evolve to reduce confusion, which will calm the currents in this swaying ocean of “big data.”

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